This paper proposes a novel video-based vehicle detection approach with data-driven adaptive neuro-fuzzy networks. The key ideas include configuring several virtual loops as vehicle detection zones in the image, assuming moving vehicles will cause pixel intensities and local textures to change, and then identifying such changes to detect vehicles. In this work, vehicle detection is treated as a pattern classification problem. First, 14 image features (regarding foreground area, texture change, and environmental condition) are extracted to represent the distinction between vehicle and nonvehicle patterns. Then, three neuro-fuzzy networks are trained via incremental semi-supervised learning to build a data-driven adaptive classifier, which judges whether a vehicle is located in the virtual loop. The semi-supervised learning procedure is performed based on a modified tri-training approach, to automatically optimize the structures and parameters of the component neuro-fuzzy networks. Experimental results illustrate that the proposed approach is accurate and robust to detect vehicles in complex environments (e.g. adverse illumination and weather conditions), and thus can improve the performance of video-based vehicle detection.